147 citations · 719 across the 43 of their papers we have counts for
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Convolutional State Space Models for Long-Range Spatiotemporal Modeling
Jimmy T. H. Smith, Shalini De Mello, Jan Kautz +2
Effectively modeling long spatiotemporal sequences is challenging due to the need to model complex spatial correlations and long-range temporal dependencies simultaneously. ConvLST…
Inferring Inference
Rajkumar Vasudeva Raju, Zhe Li, Scott Linderman +1
Patterns of microcircuitry suggest that the brain has an array of repeated canonical computational units. Yet neural representations are distributed, so the relevant computations m…
NAS-X: Neural Adaptive Smoothing via Twisting
Dieterich Lawson, Michael Li, Scott Linderman
Sequential latent variable models (SLVMs) are essential tools in statistics and machine learning, with applications ranging from healthcare to neuroscience. As their flexibility in…
Switching Autoregressive Low-rank Tensor Models
Hyun Dong Lee, Andrew Warrington, Joshua I. Glaser +1
An important problem in time-series analysis is modeling systems with time-varying dynamics. Probabilistic models with joint continuous and discrete latent states offer interpretab…
Revisiting Structured Variational Autoencoders
Yixiu Zhao, Scott W. Linderman
Structured variational autoencoders (SVAEs) combine probabilistic graphical model priors on latent variables, deep neural networks to link latent variables to observed data, and st…